Research of the processing technology for time complex event based on LSTM

With the huge amount of data, it is increasingly meaningful to combine different business system data with potential values. In the traditional event description, the input event flow of the event engine is a single atomic event type. The event predicate constraint contains simple attribute value, comparison operation and simple aggregation operation. The time constraint between events always simply. This makes the traditional detection method cannot meet the requirements such as financial, medical and other relatively accurate time requirements, event predicate constraints require more complex applications. Thus, this paper introduces the long short-term memory network model (LSTM), designs a multivariate event input to process these data based on TCN quantitative timing constraint representation model and predicate constraint representation model. In this paper, an innovative method makes the complex event processing technology more high efficient. By the analysis 200 million records of 2045 stocks, the results show that the processing technology of the complex events is more effective, more efficient.

[1]  Yanlei Diao,et al.  High-performance complex event processing over streams , 2006, SIGMOD Conference.

[2]  Jaegwon Kim,et al.  Blocking Causal Drainage and Other Maintenance Chores with Mental Causation , 2003 .

[3]  George Papastefanatos,et al.  Parallel meta-blocking for scaling entity resolution over big heterogeneous data , 2017, Inf. Syst..

[4]  Bela Stantic,et al.  OECEP: enriching complex event processing with domain knowledge from ontologies , 2012, BCI '12.

[5]  Johannes Gehrke,et al.  Cayuga: a high-performance event processing engine , 2007, SIGMOD '07.

[6]  Thomas Ertl,et al.  ScatterBlogs2: Real-Time Monitoring of Microblog Messages through User-Guided Filtering , 2013, IEEE Transactions on Visualization and Computer Graphics.

[7]  Jae-Gil Lee,et al.  APAM: Adaptive Eager-Lazy Hybrid Evaluation of Event Patterns for Low Latency , 2016, CIKM.

[8]  Rolf Isermann,et al.  Model-based fault-detection and diagnosis - status and applications , 2004, Annu. Rev. Control..

[9]  Yi Yang,et al.  Semantic Pooling for Complex Event Analysis in Untrimmed Videos , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[10]  Ling Liu,et al.  Efficient Multipattern Event Processing Over High-Speed Train Data Streams , 2015, IEEE Internet of Things Journal.

[11]  Jürgen Schmidhuber,et al.  Long Short-Term Memory , 1997, Neural Computation.

[12]  M. S. Shahriar,et al.  Characterization of the LIGO detectors during their sixth science run , 2014, 1410.7764.

[13]  Vipin Kumar,et al.  Trends in big data analytics , 2014, J. Parallel Distributed Comput..

[14]  Yogesh L. Simmhan,et al.  Knowledge-infused and consistent Complex Event Processing over real-time and persistent streams , 2017, Future Gener. Comput. Syst..

[15]  S. Klimenko,et al.  Advanced LIGO , 2014, 1411.4547.

[16]  Elke A. Rundensteiner,et al.  Complex event pattern detection over streams with interval-based temporal semantics , 2011, DEBS '11.

[17]  Alessandro Margara,et al.  Processing flows of information: From data stream to complex event processing , 2012, CSUR.

[18]  Yi Yang,et al.  Complex Event Detection using Semantic Saliency and Nearly-Isotonic SVM , 2015, ICML.

[19]  Rajeev Rastogi,et al.  Data Stream Management: Processing High-Speed Data Streams (Data-Centric Systems and Applications) , 2019 .

[20]  Neil Immerman,et al.  On complexity and optimization of expensive queries in complex event processing , 2014, SIGMOD Conference.

[21]  Edward Curry,et al.  Approximate Semantic Matching of Events for the Internet of Things , 2014, ACM Trans. Internet Techn..

[22]  Meng Yo Operator-Based Extendable Complex Event Processing Model , 2014 .